Skip to content

Update content.md #483

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 19, 2022
Merged

Update content.md #483

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
72 changes: 36 additions & 36 deletions ...dware/04.pro/shields/portenta-vision-shield/tutorials/blob-detection/content.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ title: Blob Detection with Portenta and OpenMV
coverImage: assets/por_openmv_bt_cover.svg
difficulty: intermediate
tags: [OpenMV, Blob Detection, Machine Vision, Machine Learning]
description: This tutorial will show you how to use the vision carrier board for Portenta to detect the presence and the position of objects in a camera image.
description: This tutorial will show you how to use the Portenta Vision Shield to detect the presence and the position of objects in a camera image.
author: Sebastian Romero
hardware:
- hardware/04.pro/boards/portenta-h7
Expand All @@ -13,12 +13,12 @@ software:
---

## Overview
In this tutorial you will use the vision carrier board for Portenta to detect the presence and the position of objects in a camera image. For that you will use a technique that is often referred to as blob detection. For this task you will write a MicroPython script and run it on the Portenta with the help of the OpenMV IDE.
In this tutorial you will use the Portenta Vision Shield to detect the presence and the position of objects in a camera image. To achieve this goal, you will use a technique that is often referred to as blob detection. You will write a MicroPython script and run it on the Portenta H7 with the help of the OpenMV IDE.

## Goals

- How to use the OpenMV IDE to run MicroPython on Portenta
- How to use the built-in blob detection algorithm of OpenMV
- How to use the built-in blob detection OpenMV algorithm
- How to use MicroPython to toggle the built-in LEDs

### Required Hardware and Software
Expand All @@ -31,9 +31,9 @@ In this tutorial you will use the vision carrier board for Portenta to detect th
- OpenMV IDE 2.6.4+

## Portenta and the OpenMV IDE
The OpenMV IDE was built for Machine Vision applications. It is meant to provide an Arduino like experience for simple computer vision tasks using a camera sensor. OpenMV comes with its own firmware that is built on MicroPython. Among other hardware it supports the Portenta board. A statement from the creators of OpenMV on why they built it:
The OpenMV IDE is built for Machine Vision applications. It is meant to provide an Arduino like experience for simple computer vision tasks using a camera sensor. OpenMV comes with its own firmware that is built on MicroPython. Among other hardware, it supports the Portenta board. A statement from the creators of OpenMV on the reason why they built it:

>Currently, doing anything serious involving computer vision requires a computer running an operating system running may layers of software and requiring much setup before you can get computer vision code working. This is all well and fine if you need to do many other things than just processing images, like connecting to the internet, running many different applications concurrently, etc.
>Currently, doing anything serious involving computer vision requires a computer running an operating system running many layers of software and requiring much setup before you can get computer vision code working. This is all well and fine if you need to do many other things than just processing images, like connecting to the internet, running many different applications concurrently, etc.
>
>But, what if, I just want to make an LED turn on if a red object appears in front of a white wall. Why do a need to build up a complex system for that? Or, what if I just want to turn on an LED when a face is in view?

Expand Down Expand Up @@ -62,36 +62,36 @@ Follow the instructions of the installer.

Connect the Portenta to your computer via the USB-C cable if you haven't done so yet. Make sure you first update the bootloader to the latest version using the **STM32H747_updateBootloader** sketch in the examples menu in the Arduino IDE.

Instructions on how to update the bootloader can be found in the [Updating the Portenta Bootloader](https://www.arduino.cc/pro/tutorials/portenta-h7/updating-the-bootloader) tutorial.
Instructions on how to update the bootloader can be found in the [Updating the Portenta Bootloader](https://docs.arduino.cc/tutorials/portenta-h7/updating-the-bootloader) tutorial.

***In bootloader versions 17 and older there was a bug that could put the Portenta in a boot loop when the transmission aborted while flashing a large firmware file. This was fixed in the bootloader version 18. We strongly advise to update the bootloader before you proceed with the next step of this tutorial!***
***In bootloader versions 17 and older, there was a bug that could put the Portenta in a boot loop when the transmission aborted while flashing a large firmware file. This was fixed in the bootloader version 18. We strongly advise to update the bootloader before you proceed with the next step of this tutorial!***

After updating the bootloader put the Portenta in bootloader mode by double pressing the reset button on the board. The built-in green LED will start fading in and out. Now open the OpenMV IDE.
After updating the bootloader, put the Portenta in bootloader mode by double pressing the reset button on the board. The built-in green LED will start fading in and out. Now open the OpenMV IDE.

![The OpenMV IDE after starting it](assets/por_openmv_open_ide.png)

Click on the "connect" symbol at the bottom of the left toolbar.

![Click the connect button to attach the Portenta to the OpenMV IDE](assets/por_openmv_click_connect.png)

A pop-up will ask you how you would like to proceed "DFU bootloader(s) found. What would you like to do?". Select "Reset Firmware to Release Version". This will install the latest OpenMV firmware on the Portenta. If it asks you whether it should erase the internal file system you can click "No".
A pop-up will ask you how you would like to proceed: "DFU bootloader(s) found. What would you like to do?". Select "Reset Firmware to Release Version". This will install the latest OpenMV firmware on the Portenta. If it asks you whether it should erase the internal file system you can click "No".

![Install the latest version of the OpenMV firmware](assets/por_openmv_reset_firmware.png)

Portenta's green LED will start flashing while the OpenMV firmware is being uploaded to the board. A terminal window will open which shows you the upload progress. Wait until the green LED stops flashing and fading. You will see a message saying "DFU firmware update complete!" when the process is done.
Portenta's green LED will start flashing while the OpenMV firmware is being uploaded on the board. A terminal window will open showing you the upload progress. Wait until the green LED stops flashing and fading. You will see a message saying "DFU firmware update complete!" when the process is done.

***Installing the OpenMV firmware will overwrite any existing sketches in the internal Flash of Portenta. As a result the M7 port won't be exposed in the Arduino IDE anymore. To re-flash the M7 with an Arduino firmware you need to put the board into bootloader mode. To do so double press the reset button on the Portenta H7 board. The built-in green LED will start fading in and out. In bootloader mode you will see the Portenta M7 port again in the Arduino IDE.***
***Installing the OpenMV firmware will overwrite any existing sketches in the internal Flash of Portenta. As a result the M7 port will not be exposed in the Arduino IDE anymore. To re-flash the M7 with an Arduino firmware, you need to put the board into bootloader mode. To do so, double press the reset button on the Portenta H7 board. The built-in green LED will start fading in and out. In bootloader mode you will see the Portenta M7 port again in the Arduino IDE.***

The Portenta will start flashing its blue LED when it's ready to be connected. After confirming the completion dialog the Portenta should already be connected to the OpenMV IDE, otherwise click the "connect" button once again.
The Portenta will start flashing its blue LED when it is ready to be connected. After confirming the completion dialog, the Portenta should already be connected to the OpenMV IDE, otherwise click the "connect" button once again.

![When the Portenta is successfully connected to the OpenMV IDE a green play button appears in the lower left](assets/por_openmv_board_connected.png)


## Blob Detection

In this section you will learn how to use the built-in blob detection algorithm to detect the location of objects in an image. That algorithm allows to detect areas in a digital image that differ in properties such as brightness or color compared to surrounding areas. These areas are called blobs.
In this section you will learn how to use the built-in blob detection algorithm to detect the location of objects in an image. That algorithm allows to detect areas in a digital image that differ in properties, such as brightness or color compared to surrounding areas. These areas are called blobs.

To do so you need to feed an image from the camera to the algorithm. It will then analyze it and output the coordinates of the found blobs. You will visualize these coordinates directly on the image and indicate whether a blob was found by using the red and green LED.
To do so, you need to feed an image from the camera to the algorithm. It will then analyze it and output the coordinates of the found blobs. You will visualize these coordinates directly on the image and indicate whether a blob was found by using the red and green LED.

### 1. Prepare the Script

Expand All @@ -104,11 +104,11 @@ import image # Import module containing machine vision algorithms
import time # Import module for tracking elapsed time
```

A module in Python is a confined bundle of functionality. By importing it into the script it gets made available.
A module in Python is a confined bundle of functionality. By importing it into the script, it gets made available.

### 2. Preparing the Sensor

In order to take a snapshot with the camera it has to be configured in the script.
In order to take a snapshot with the camera, it has to be configured in the script.

```python
sensor.reset() # Resets the sensor
Expand All @@ -117,24 +117,24 @@ sensor.set_framesize(sensor.QVGA) # Sets the resolution to 320x240 px
sensor.skip_frames(time = 2000) # Skip some frames to let the image stabilize
```

The most relevant functions in this snipped are `set_pixformat` and `set_framesize`. The camera that comes with the Portenta Vision Carrier only supports grey scale images. Therefore we need to set it via the `sensor.GRAYSCALE` parameter.
The most relevant functions in this snipped are `set_pixformat` and `set_framesize`. The camera that comes with the Portenta Vision Shield only supports grey scale images. Therefore, you need to set it via the `sensor.GRAYSCALE` parameter.

The resolution of the camera needs to be set to a supported format both by the sensor and the algorithm. Algorithms which use a neural network are usually trained on a specific image resolution. This makes them sensitive to the provided image snapshot resolution. The vision carrier supports `QVGA` which you will use in this tutorial.
The resolution of the camera needs to be set to a supported format both by the sensor and the algorithm. Algorithms which use a neural network are usually trained on a specific image resolution, to make them sensitive to the provided image snapshot resolution. The Portenta Vision Shield supports `QVGA`, which you will use in this tutorial.

### 3. Detecting Blobs

In order to feed the blob detection algorithm with an image you have to take a snapshot from the camera or load the image from memory (e.g. SD card or internal Flash). In this case you will take a snapshot using the `snapshot()` function. The resulting image needs then to be fed to the algorithm using the `find_blobs` function. You will notice that a list of tuples gets passed to the algorithm. In this list you can specify the grey scale values (brightness) that are mostly contained in the object that you would like to track. If you were for example to detect white objects on a black background the resulting range of brightness would be very narrow (e.g. from 200 - 255). Remember that 255 denotes the maximum brightness / white and 0 corresponds to the minimum brightness / black. If we're interested in a wider range of grey scale values to detect various objects we can set the threshold range for example to (100, 255).
In order to feed the blob detection algorithm with an image, you have to take a snapshot from the camera or load the image from memory (e.g. SD card or internal Flash). In this case, you will take a snapshot using the `snapshot()` function. The resulting image needs then to be fed to the algorithm using the `find_blobs` function. You will notice that a list of tuples gets passed to the algorithm. In this list, you can specify the grey scale values (brightness) that are mostly contained in the object that you would like to track. For instance, if you wanted to detect white objects on a black background, the resulting range of brightness would be very narrow (e.g. from 200 - 255). Remember that 255 denotes the maximum brightness / white and 0 corresponds to the minimum brightness / black. If you are interested in a wider range of grey scale values to detect various objects, you can set the threshold range for example to (100, 255).

```python
thresholds = (100, 255) # Define the min/max gray scale values we're looking for
img = sensor.snapshot() # Takes a snapshot and saves it in memory
thresholds = (100, 255) # Define the min/max gray scale values you are looking for
img = sensor.snapshot() # Takes a snapshot and saves it in the memory

# Find blobs with a minimal area of 15x15 = 200 px
# Overlapping blobs won't be merged
# Overlapping blobs will not be merged
blobs = img.find_blobs([thresholds], area_threshold=225, merge=False)
```

Once the blobs are detected you may be interested to see where in the images they were found. This can be done by drawing directly on the camera image.
Once the blobs are detected, you may be interested to see where they have been found in the images. This can be done by drawing directly on the camera image.

```python
# Draw blobs
Expand All @@ -152,14 +152,14 @@ The result of that will be visible in the Frame Buffer preview panel on the righ
What if you want some visual feedback from the blob detection without any computer connected to your Portenta? You could use for example the built-in LEDs to indicate whether or not a blob was found in the camera image. Let's initialize the red and the green LEDs with the following code:

```python
ledRed = pyb.LED(1) # Initiates the red led
ledGreen = pyb.LED(2) # Initiates the green led
ledRed = pyb.LED(1) # Initializes the red LED
ledGreen = pyb.LED(2) # Initializes the green LED
```

And then add the logic that will turn on the appropriate LED if a blob is present. This part of the code will be added after the "Draw Blobs" logic.

```python
# Turn on green LED if a blob was found
# Turn the green LED on if a blob was found
if len(blobs) > 0:
ledGreen.on()
ledRed.off()
Expand All @@ -169,7 +169,7 @@ else:
ledRed.on()
```

In this example the green LED will light up when there is at least one blob found in the image. The red LED will light up if no blob could be found.
In this example, the green LED will light up when there is at least one blob found in the image. The red LED will light up if no blob could be found.

### 5. Uploading the Script
Let's program the Portenta with the complete script and test if the algorithm works. Copy the following script and paste it into the new script file that you created.
Expand All @@ -186,8 +186,8 @@ sensor.set_framesize(sensor.QVGA) # Sets the resolution to 320x240 px
sensor.skip_frames(time = 2000) # Skip some frames to let the image stabilize

thresholds = (100, 255) # Define the min/max gray scale values we're looking for
ledRed = pyb.LED(1) # Initiates the red led
ledGreen = pyb.LED(2) # Initiates the green led
ledRed = pyb.LED(1) # Initializes the red LED
ledGreen = pyb.LED(2) # Initializes the green LED

clock = time.clock() # Instantiates a clock object

Expand All @@ -206,7 +206,7 @@ while(True):
# Draw a cross in the middle of the blob
img.draw_cross(blob.cx(), blob.cy(), color=255)

# Turn on green LED if a blob was found
# Turn the green LED on if a blob was found
if len(blobs) > 0:
ledGreen.on()
ledRed.off()
Expand All @@ -223,25 +223,25 @@ while(True):

Click on the "Play" button at the bottom of the left toolbar. Place some objects on your desk and check if the Portenta can detect them.

***The MicroPython script doesn't get compiled and linked into an actual firmware. Instead it gets copied to the internal Flash of the Portenta where it gets compiled and executed on the fly.***
***The MicroPython script does not get compiled and linked into an actual firmware. Instead it gets copied to the internal Flash of the Portenta where it gets compiled and executed on the fly.***

![An example of a blob detection running in the OpenMV IDE](assets/por_openmv_blob_detected.png)

## Conclusion

In this tutorial you learned how to use the OpenMV IDE to develop MicroPython scripts that then run on the Portenta board. You also learned how to configure the camera of the Vision Carrier board to be used for machine vision applications in OpenMV. Last but not least you learned how to interact with the built-in LEDs in MicroPython on the OpenMV firmware.
In this tutorial you learned how to use the OpenMV IDE to develop MicroPython scripts that can run on the Portenta board. You also learned how to configure the camera of the Portenta Vision Shield for machine vision applications in OpenMV. Last but not least, you learned how to interact with the built-in LEDs in MicroPython on the OpenMV firmware.

### Next Steps

- Familiarize yourself with the OpenMV IDE. There are many other features that didn't get mentioned in this tutorial (e.g. the Serial Terminal).
- Familiarize yourself with the OpenMV IDE. There are many other features that have not been mentioned in this tutorial (e.g. the Serial Terminal).
- Try out other machine vision examples that come with the OpenMV IDE (e.g. Face Detection). You can find them in the "Examples" menu.

## Troubleshooting

### OpenMV Firmware Flashing Issues

- If the upload of the OpenMV firmware fails during the download, put the board back in boot loader mode and try again. Give it a few tries until the firmware gets successfully uploaded.
- If the upload of the OpenMV firmware fails during the download, put the board back in bootloader mode and try again. Give it a few trials until the firmware gets successfully uploaded.
- If the upload of the OpenMV firmware fails without even starting, try uploading the latest firmware using the "Load Specific Firmware File" option. You can find the latest firmware on the [OpenMV Github repository](https://github.com/openmv/openmv/releases). Look for a file called **firmware.bin** in the PORTENTA folder.
- If you experience issues putting the board in bootloader mode, make sure you first update the bootloader to the latest version using the **STM32H747_updateBootloader** sketch from the examples menu in the Arduino IDE.
- If the camera cannot get recognized by the OpenMV IDE or if you see a "No OpenMV Cams found!" message, press the reset button of Portenta once and wait until you see the blue LED flashing. Then try again connecting to the board.
- If you experience issues putting the board into bootloader mode, make sure you first updated the bootloader to the latest version using the **STM32H747_updateBootloader** sketch from the examples menu in the Arduino IDE.
- If the camera cannot get recognized by the OpenMV IDE or if you see a "No OpenMV Cams found!" message, press the reset button on the Portenta once and wait until you see the blue LED flashing. Then try again connecting to the board.
- If you see a "OSError: Reset Failed" message, reset the board by pressing the reset button. Wait until you see the blue LED flashing, connect the board to the OpenMV IDE and try running the script again.